Small Scott R, Chan Shing, Walmsley Rosemary, von Fritsch Lennart, Acquah Aidan, Mertes Gert, Feakins Benjamin G, Creagh Andrew, Strange Adam, Matthews Charles E, Clifton David A, Price Andrew J, Khalid Sara, Bennett Derrick, Doherty Aiden
Nuffield Department of Population Health, University of Oxford, UK.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK.
medRxiv. 2023 Feb 22:2023.02.20.23285750. doi: 10.1101/2023.02.20.23285750.
Step count is an intuitive measure of physical activity frequently quantified in a range of health-related studies; however, accurate quantification of step count can be difficult in the free-living environment, with step counting error routinely above 20% in both consumer and research-grade wrist-worn devices. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and to assess its association with cardiovascular and all-cause mortality in a large prospective cohort study.
We developed and externally validated a hybrid step detection model that involves self-supervised machine learning, trained on a new ground truth annotated, free-living step count dataset (OxWalk, n=39, aged 19-81) and tested against other open-source step counting algorithms. This model was applied to ascertain daily step counts from raw wrist-worn accelerometer data of 75,493 UK Biobank participants without a prior history of cardiovascular disease (CVD) or cancer. Cox regression was used to obtain hazard ratios and 95% confidence intervals for the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders.
The novel step algorithm demonstrated a mean absolute percent error of 12.5% in free-living validation, detecting 98.7% of true steps and substantially outperforming other recent wrist-worn, open-source algorithms. Our data are indicative of an inverse dose-response association, where, for example, taking 6,596 to 8,474 steps per day was associated with a 39% [24-52%] and 27% [16-36%] lower risk of fatal CVD and all-cause mortality, respectively, compared to those taking fewer steps each day.
An accurate measure of step count was ascertained using a machine learning pipeline that demonstrates state-of-the-art accuracy in internal and external validation. The expected associations with CVD and all-cause mortality indicate excellent face validity. This algorithm can be used widely for other studies that have utilised wrist-worn accelerometers and an open-source pipeline is provided to facilitate implementation.
步数是身体活动的一种直观度量，在一系列与健康相关的研究中经常被量化；然而，在自由生活环境中准确量化步数可能很困难，无论是消费级还是研究级的腕戴设备，步数计数误差通常都超过20%。本研究旨在描述源自腕戴式加速度计的步数计数的开发与验证，并在一项大型前瞻性队列研究中评估其与心血管疾病和全因死亡率的关联。
我们开发并在外部验证了一种混合步数检测模型，该模型涉及自我监督机器学习，在一个新的带有地面真值注释的自由生活步数计数数据集（OxWalk，n = 39，年龄19 - 81岁）上进行训练，并与其他开源步数计数算法进行对比测试。该模型应用于从75493名无心血管疾病（CVD）或癌症病史的英国生物银行参与者的原始腕戴式加速度计数据中确定每日步数。在对潜在混杂因素进行调整后，使用Cox回归获得每日步数与致命性CVD和全因死亡率关联的风险比及95%置信区间。
这种新型步数算法在自由生活验证中显示出平均绝对百分比误差为12.5%，能检测到98.7%的真实步数，显著优于其他近期的腕戴式开源算法。我们的数据表明存在剂量反应负相关，例如，与每天步数较少的人相比，每天走6596至8474步分别使致命性CVD风险和全因死亡率降低39%[24 - 52%]和27%[16 - 36%]。
使用机器学习流程确定了一种准确的步数测量方法，该方法在内部和外部验证中均展示了最先进的准确性。与CVD和全因死亡率的预期关联表明其具有良好的表面效度。该算法可广泛应用于其他使用腕戴式加速度计的研究，并提供了一个开源流程以促进实施。
